In silico Prediction of Total Human Plasma Clearance

Abstract

The prediction of the total human plasma clearance of novel chemical entities continues to be of paramount importance in drug design and optimization, because it impacts both dose size and dose regimen. Although many in vivo and in vitro methods have been proposed, a well-constructed, well-validated, and less resource-intensive computational tool would still be very useful in an iterative compound design cycle. A new completely in silico linear PLS (partial least-squares) model to predict the human plasma clearance was built on the basis of a large data set of 754 compounds using physicochemical descriptors and structural fragments, the latter able to better represent biotransformation processes. The model has been validated using the "ELASTICO" approach (Enhanced Leave Analog-Structural, Therapeutic, Ionization Class Out) based on ten therapeutic/structural analog classes. The model yields a geometric mean fold error (GMFE) of 2.1 and a percentage of compounds predicted within 2- and 3-fold error of 59% and 80%, respectively, showing an improved performance when compared with previous published works in predicting clearance of neutral compounds, and a very good performance with ionized molecules at pH 7.5, able to compare favorably with fairly accurate in vivo methods.